Method and apparatus for expanding the use of existing computer-aided detection code

ABSTRACT

A method and apparatus for analyzing a medical image obtained from one of a plurality of digital modalities, the method comprising transforming or mapping the initial medical image to create a uniform contrast response and appearance regardless of the original modality of the image.

The present patent application is a Continuation of application Ser. No.10/079,327, filed Feb. 19, 2002 now U.S. Pat. No. 7,072,498.

RELATED CASES

This application is a continuation-in-part of U.S. patent applicationSer. No. 09/992,059 filed Nov. 21, 2001, now U.S. Pat. No. 7,054,473 andincorporates that application in its entirety by reference.

FIELD OF THE INVENTION

The present invention relates to a method for training or tuning acomputer-aided diagnosis (CAD) system, and more specifically, toexpanding the use of an existing CAD system to digitally acquiredimages.

BACKGROUND

It is a well-known fact in the computer-aided detection (CAD) researchcommunity that proper tuning and training of a pattern recognition code(such as the CAD code referred to in this invention) requires a largedatabase of training cases. Anil K. Jain and Richard C. Dubes,“Algorithms Clustering Data”, Prentice Hall, March 1988 contains adiscussion of the requirements on the number of training examples as afunction of degrees of freedom. Some review papers that describe theconcepts of feature extraction and classification by neural networks ina CAD application are: Matt Kupinski et al., “Computerized detection ofmammographic lesions: Performance of artificial neural network withenhanced feature extraction”, SPIE Vol 2434, p 598, and Maryellen Gigerand Heber MacMahon, “Image Processing and Computer-aided Diagnosis”,RSNA Vol. 34, N 3, May 1996)

A large database is needed for two reasons. First, abnormalities such aslesions in mammograms have a wide spectrum of differing appearances, andthe training database should contain examples of all types. Second,these codes typically contain both rule-based criteria and neuralnetwork classifiers to reduce the number of false positives, and theproper values of all parameters used in these rules and classifiersdepends on having seen many more training examples than there arenumbers of parameters, or features, in order to avoid “overtraining”, or“over-optimizing,” the tendency of the code to memorize its trainingdata.

A rule of thumb is that one should have at least 10 times more trainingcases than the degrees of freedom in the decision making code. Anotherconservative practice is to separate the training database from the testdatabase, and maintain absolute independence in order to avoid biasedperformance results. In a study performed by Burhenne et. al. (Burhenneet. al., Potential Contribution of Computer-aided Detection to theSensitivity of Screening Mammography, Radiology, May 2000, p 554-562)performance of a particular CAD code was tested on an independentdatabase of 1083 breast cancers. This particular code was “tuned”, or“trained”, on a “training database” of approximately 1500 cancer cases.

FIG. 10 shows an example of the use of a rule to separate true lesionsfrom false positives. In this example, one feature is plotted versusanother feature in a scatter plot. The true lesions 1020 appear on thisscatter plot as dark x's, the false positives as light dots 1030. It isapparent that the true lesions tend to cluster in a band near the centerof the scatter plot, while the false positives are mostly in a verticalcluster below the true positives. By using the dark dashed line 1010 asa “decision surface”, and accepting only the marks above the line, mostof the false positives will be eliminated while most of the truepositives are retained. It can be appreciated that the more “training”,or “example” cases one has, the better the line or decision surface willbe placed, i.e. the more optimal the separation of the true lesions fromthe false positives. In practice, a typical CAD code may have dozens ofsuch rules, as well as a classifier to allow decisions to be made in acomplex multi-featured decision space.

Recently a new mammographic x-ray detector has been approved by the FDA:the Senograph 2000 produced by GE. This product will soon be followed byother similar digital detectors produced by such companies as Lorad,Fisher, Siemens, and Fuji. In the field of chest radiography, digitaldetectors have been available for some time. Now, there is a verycritical barrier to the use of CAD codes applied specifically to themedical images obtained by these new detectors. This is the fact thatthe devices have been in existence for such a short time that the numberof cancer cases taken and archived is not yet sufficient to train ortune these codes. The number of cancers in existence detected in thesedigital detectors is not yet sufficient to even test these codes withgreat confidence. Using CAD codes on direct digital medical images withany confidence therefore requires a method to obtain parameters andfeature values needed by the code from a source other than the smallnumber of existing cases.

SUMMARY OF THE INVENTION

A method and apparatus for analyzing a medical image obtained from oneof a plurality of digital modalities, the method comprising transformingor mapping the initial medical image to create a uniform contrastresponse and appearance regardless of the original modality of theimage.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 is a block diagram of a network that may be used with the presentinvention.

FIG. 2A is a block diagram of one embodiment of the training systeminitially used to train the CAD code.

FIG. 2B is a block diagram of one embodiment of the system to generatethe lookup table for converting images.

FIG. 2C is a block diagram of one embodiment of the image analysissystem used to convert images to the “standard canonical format.”

FIG. 3 is a flowchart of using the present process.

FIG. 4 is a flowchart of one embodiment of obtaining a converter forimages from digital response to the canonical.

FIGS. 5A and 5B illustrate characteristic curves for two types of film.

FIG. 5C shows a typical response curve for a digitizing scanner, whicheffectively transforms film OD to pixel value.

FIG. 6 illustrates a characteristic curve for a digital detector.

FIG. 7 is an illustration of a step wedge used to calibrate the responseof the detectors.

FIG. 8 is a mapping of the pixel values from digital to digitized filmbased images.

FIGS. 9A-C illustrate the differences between images acquired usingfilm, images acquired using a digital detector, and the digitallyacquired images after transformation.

FIG. 10 illustrates an example of using a rule to separate out falsepositives from a set of detected anomalies.

DETAILED DESCRIPTION

A method and apparatus for analyzing a medical image obtained from oneof a plurality of modalities is described. The method transforms theinitial medical image to create the same contrast response andappearance regardless of the original modality of the image. Thispermits the use of computer aided diagnosis (CAD) code that was trainedon film based, or other input based images. This simplifies theadaptation of a new film, or of digital imaging systems withoutrequiring an extended period to obtain an adequate set of images of thenew type for training and tuning the CAD code. Since the number of testcases needed for tuning far exceeds the number available for newlyadapted systems, the present invention permits the faster conversion tonew technologies, such as film to digital in radiology departments.

FIGS. 9A-C illustrate the differences between images acquired usingfilm, images acquired using a digital detector, and the digitallyacquired images after transformation. As can be seen, the originalimage, FIG. 9A, which was used to train the CAD code has a certain grayscale level and abnormalities would have an expected contrast withrespect to their surroundings. The digitally acquired image, FIG. 9B hasa substantially different contrast. Therefore, it would be expected thatCAD code that had been trained on the original image (FIG. 9A) would notperform correctly on the digitally acquired image (FIG. 9B). However,the conversion, discussed in more detail below, converts the digitalimage (FIG. 9B) to a digital image having the “standard canonical form”shown in FIG. 9C. As can be seen, the transformed/remapped image of FIG.9C is much closer in gray scale levels and contrast to the originalimage (FIG. 9A). Thus, the CAD code, trained on original images like theone shown in FIG. 9A can easily be used to detect abnormalities on theremapped image of FIG. 9C. This process or remapping, as well as thetuning process to improve the CAD code is described in more detailbelow.

FIG. 1 is a block diagram of a network that may be used with the presentinvention. The system includes one or more image acquisition modules130A, 130B. The image acquisition modules may be conventional film imageacquisition systems, which are known in the art, and/or digital imageacquisition systems. Standard methods may be used to obtain the analogor digital images, whether two or three-dimensional. The outputs of theimage acquisition modules 130A, 130B, are digital or analog images. Oneexample of a film based image acquisition system 130A is described inWang, U.S. Pat. No. 5,828,774.

These images are passed to image analysis system 120. For oneembodiment, the images are sent through network 110 to image analysissystem 120. Network 110 may be an internal local area network (LAN), awide area network (WAN), the Internet, or any other type of network. Forone embodiment, if the network 110 is not a local internal network, thenthe images sent by image acquisition modules 130A, 130B are encrypted orin some other way protected to ensure the patient's privacy. Thispermits the use of a centralized image analysis system 120 which mayreceive images from multiple offices that may be located anywhere in theworld. Similarly, the analyzed images/output may be sent to reviewstations anywhere in the world.

The image analysis system 120 performs the preprocessing, recognition,and/or post-processing of the images. The image analysis system 120 isdescribed in more detail below.

The system, for one embodiment, further includes a HIS/RIS (hospitalinformation system/radiology information system) system 170. The HIS/RISsystem 170 is coupled to the image analysis system 120, either directlyor through network 110. The HIS/RIS system 170 provides patient data, inone of a variety of formats. For one embodiment, the HIS/RIS system 170may provide data in the HL7 format. Alternative formats may be used. Theimages processed by image analysis system 120 may be stored within apatient record, in the HL7 format. For another embodiment, the image maybe stored in DICOM format, including the appropriate patientinformation.

For one embodiment, a copy of the processed images is stored in systemarchive 140, permitting retrieval of the image. For one embodiment, alower resolution image is stored. For one embodiment, the stored imagedoes not include any tagging or other indicators added by image analysissystem 120. For another embodiment, the owner of the system may selectthe format of the images stored in system archive 140.

The images are displayed to a reviewer at review station 150. Reviewstations 140 may be directly coupled to image analysis system 120, orcoupled through a network. For one embodiment, the images may further beviewed at remote viewing stations 160. Remote viewing stations 160 maybe conventional computer systems coupled to the network 110, may behandheld devices, laptop computers, or any other display mechanism. Theremote viewing stations 160 may be wirelessly linked to the network, topermit fully mobility. This permits a doctor in a remote location toreview the images, and may be used to allow the patient or others toreview the images remotely. Thus, for example, a radiologist at acentral location may initially review and analyze the images, annotatethem. Then, the images, and notation—or a report generated based on theimages and notation—is sent to a remote system where the doctor canreview the data with the client.

For one embodiment, the images, report, or other output may be sent to aprinter 180. The printer 180, for one embodiment, may print to film, topermit conventional review of the enhanced images. For one embodiment,the printer 180 may print multiple images, for example, one set oforiginal images, a set of enhanced images, and a set of enhanced imageswith markers indicating the abnormalities found by the image analysissystem 120. The printer 180 may be coupled to the image analysis system120 and/or the system archive 140 either directly or through network110. As discussed above with respect to the review stations 150, 160,the printer 180 need not be in the same location as the image analysissystem 120.

Of course, not all of these elements must be present in order toimplement the present system. At its simplest, the system includes animage acquisition module 130A, an image analysis system 120, and areview station 150 that permits viewing of the images. These systems120, 130A, 150 may be coupled directly, without the use of a network110. At its most complex, the system may be a distributed system havingimage acquisition modules 130A, 130B at various remote locations, whilea central archive 140 and one or more image analysis systems 120 areused to process the acquired images. Then, the images may be sent tovarious local or remote review stations 150, 160. Note that although theimage analysis system 120 illustrated as once central device, it may bea distributed system.

FIG. 2A is a block diagram of one embodiment of the training systeminitially used to train the CAD code. The CAD code 205 has been trainedon a database of digitized medical images 215, where the original filmshave been digitized by a laser or CCD scanner. If this database is largeenough the parameters used by the code can be optimized such that theperformance, measured by metrics such as sensitivity and specificity, isstable, well characterized, and gives a good indication of the code'sperformance on future cases input to the system. This is determinedusing the testing database 220, generally also using a database ofdigitized medical images.

An important characteristic of the images for detecting abnormalitiessuch as lesions is the “characteristic” curve, which describes thedependence of the x-ray film to exposure. Typical characteristic curvesare shown in FIGS. 5A and 5B. FIG. 5A illustrates the characteristiccurve of the Kodak Insight film, often used for chest radiographs. FIG.5B illustrates the characteristic of the Kodak Min-R2000 film, oftenused for mammograms. FIGS. 5A and B show the dependence of the opticaldensity (OD) on exposure. FIG. 5C shows a typical response curve for adigitizing scanner, which effectively transforms film OD to pixel value.Upon digitizing the film, one obtains a two-dimensional array in whicheach pixel value represents the exposure striking the film screen (thedetector). The slope of the characteristic curve determines the contrastresponse, or change of output pixel value with change of incomingexposure.

The training and tuning module 210 uses the images in the trainingdatabase 215 to create parameter values and thresholds to separate realabnormalities from false positives. These “decision surfaces” arethreshold values that are used to separate one class of markedabnormalities from another based on feature values. FIG. 10 shows anexample of such a decision surface 1010, used to separate true lesionsfrom false positives for one particular type of abnormality. In thisexample one feature is plotted verses another feature in a scatter plot.The true lesions appear on this scatter plot as dark x's, the falsepositives as light dots. It is apparent that the true lesions tend tocluster in a band near the center of the scatter plot, while the falsepositives are mostly in a cluster below the true positives. By using thedark dashed line as a decision surface, and accepting only the marksabove the line, most of the false positives will be eliminated whilemost of the true positives are retained. It can be appreciated that themore “training”, or “example” cases one has, the better the line ordecision surface will be placed. In practice, a typical CAD code mayhave dozens of such rules, as well as a classifier to allow decisions tobe made in a complex multi-dimensioned decision space.

The training and tuning module 210 uses the images in the trainingdatabase 215 to create decision surfaces to separate real abnormalitiesfrom false positives. FIG. 10 shows an example of such a decisionsurface, used to separate true lesions from false positives for oneparticular type of abnormality. In this example one feature is plottedverses another feature in a scatter plot. The true lesions appear onthis scatter plot as dark x's, the false positives as light dots. It isapparent that the true lesions tend to cluster in a band near the centerof the scatter plot, while the false positives are mostly in a clusterbelow the true positives. By using the dark dashed line as a decisionsurface, and accepting only the marks above the line, most of the falsepositives will be eliminated while most of the true positives areretained. It can be appreciated that the more “training”, or “example”cases one has, the better the line or decision surface will be placed.In practice, a typical CAD code may have dozens of such rules, as wellas a classifier to allow decisions to be made in a complexmulti-dimensioned decision space.

The images in the testing database 220 are used to test and verify thedecision surfaces by running images with known abnormalities through thetrained CAD code 205 to verify that it successfully separates the trueabnormalities from false positives.

For one embodiment, an additional database, the tuning database 225 maybe added to the training cycle. The tuning database 225 includesdigitally acquired images that have been remapped to the canonicalformat. FIG. 9C shows such an image. The tuning database 225 is used tomake fine adjustments in the decision surfaces generated from film-basedimages. For example, even remapped images that are digitally acquiredmay not match exactly the characteristics of film-based images.Therefore, tuning database 225 may be used to make fine adjustments tothe decisioning, to account for any differences between film-based anddigitally acquired images. The tuning database 225 includes many fewercases than the training database 215 and the testing database 220. Forone embodiment, the tuning database 225 may be used to constantlyincrementally improve the decision surfaces, as additional digitalimages are acquired.

FIG. 2B is a block diagram of one embodiment of the system to generatethe lookup table for remapping images. In the parent application, thisprocess was called “normalization”, and a method was described for howthis can be done. The present application provides additional detailsand further illustrates the technique by providing some concreteexamples in this application.

In general, the CAD code, once developed and tuned on film datacharacterized by the response curves in FIGS. 5A and B, cannotimmediately be used to analyze data obtained by a digital detector suchas the response shown in FIG. 6. This is because the slope and interceptof the two curves are different. FIG. 6 shows a typical response of adigital detector with Log(exposure). FIG. 6 illustrates thecharacteristic response of the Kodak CR400 detector.

Because of this difference the exposure difference caused by the samelesion will result in different pixel differences when detected by thedigital detector than by film. This can be solved by determining thetransformation or mapping required to turn the response curve in FIG. 6to that of FIGS. 5A-B.

The system 230 includes a calibration design 235 which is used to createcomparison images for film-based and digitally acquired images. In FIG.7 illustrates an exemplary calibration design, a step wedge. Alternativecalibration designs may be used. The calibration design 235 is placed ontop of a film/screen detector, and a digital cassette/detector andexposed to x-rays.

For the step wedge 710 shown in FIG. 7, the varying thickness of eachstep 720 changes the attenuation of the x-rays and therefore exposes thefilm/screen or digital detector to different exposures at each step 710.The resulting pixel value at under each step on the image then follows acurve such as the responses shown in FIG. 5A, 5B, or 6.

Returning to FIG. 2B, the digitized film image 237 and digitallyacquired image 239, each exposed with the calibration design 235 areinput to table generator 240.

Step identifier 242 assigns values to the pixel values at each of thesteps. The step pixel values for the screen film system are given byPVf1, PVf2, PVf3. The step pixel values for the digital detector asPVd1, Pvd2, PVd3.

Mapper 244 plots PVf versus PVd to provide the mapping from digitaldetector pixel values to film/screen pixel values. This is what is meantby “transform” or “mapping”, or, as in the previous application,“normalization”. FIG. 8 is a mapping of the pixel values from digital todigitized film based images.

Using this transform, given image data from one type of detector, e.g.digital mammograms or digital chest, the image data can be transformedsuch that it looks like an image taken by film/screen. This may bereferred to as an image that has been “mapped” into “film space”. Whenthis has been accomplished, the CAD code can be applied to thetransformed data with reasonable confidence that the results will becomparable to that obtained by the CAD code applied to film/screen. Itcan be appreciated that it is not necessary to map the digital data intofilm space, it can, if desired, be mapped into any desired space, i.e.,mapped onto a curve having any slope and intercept. It may often bedesired for example, to map onto a space that, unlike film, has a linearresponse to log(exposure), without the non-linear sections in FIG. 8.This desired space is referred to as the “standard canonical form”. Inone embodiment, the standard canonical form is the curve thatcharacterizes the response of film images, since the CAD code wasdeveloped using data from film.

After the mapping described above, optionally, shifter 246 may be usedto shift all pixel values by a constant: PVnew=PVold−offset

The purpose of shifting is to ensure that the absolute pixel values ofthe transformed image have some desired range. For example, it may bedesirable to have the mean pixel value in the image have a constantvalue, or a lowest or highest pixel value, etc. To accomplish this, onewould calculate the mean value then and add or subtract a constant fromthe entire image to shift it to the desired value.

Table generator 248 generates a look-up table incorporating theremapping and shifting operations. For one embodiment, the user mayselect whether to include the shifting operation, through user interface250. For one embodiment, the user may alter the mapping, shift, orlook-up table as well.

The output of table generator 240 is a look-up table that may be used inthe CAD system. In general, this system 230 is implemented by themanufacturer of the CAD system, in order to generate a lookup table,which will be incorporated into the CAD system. Thus, this remappingprocess is transparent to the user of the system.

FIG. 2C is a block diagram of one embodiment of the image analysissystem used to convert images to the “standard canonical format.” OnFIG. 1, the transform mapper 260 is part of the image analysis system120. The converter illustrated in FIG. 2C corresponds to the“preprocessor” described in the parent application of this case.

The image input 255 is a digital image. The image input 255 may beobtained from a digitized film image, or from a digitally acquiredimage. The transform mapper 260 receives the image input, and usingimage origination identifier 270 determines the origin of the image. Theorigin of the image identifies whether the image was acquired from afilm, and if so what type, or from a digital detector, and if so whattype. For one embodiment, there are separate look-up tables 265 for eachtype of transformation. Thus, the system is able to transform inputsfrom a variety of sources to the “standard canonical format.” For oneembodiment, the image label identifies the origin of the image. Foranother embodiment, the image origin may be provided by a user. Foranother embodiment, a label or other type of attached identifierprovides origin data.

The remapper 280 then loads the appropriate lookup table 265, based onthe known “standard canonical format” and the known origin of the image.The remapper 280 remaps the image into the “standard canonical format.”The remapped image is passed to the CAD code 285, to perform CADprocessing on the image. As discussed above, the CAD code 285 uses thedelimiters to identify abnormalities, and remove false positives fromthe list of identified abnormalities. The output of the CAD code 285 arethe CAD results. The CAD results may be a list of abnormalities, theirclass, and location. Alternatively, the CAD results may be graphical,having graphical icons to identify each of the identified abnormalities,with an icon image identifying the type of abnormality. Alternatively,the CAD results may be a combination of the above.

In this way, the CAD system, using transform mapper 260 can use CAD code285 trained on existing film-based images to identify abnormalities indigitally acquired medical images. Note that the original format of theimage input 255 is irrelevant, because transform mapper 260 convertsimages to the “standard canonical form.” Furthermore, the standardcanonical form, as discussed above, may be set by user preference. Forone embodiment, the standard canonical form is the form of thefilm-based images that were originally used to train the CAL) code 285.

FIG. 3 is a flowchart of using the present process. For one embodiment,this process occurs automatically in the image analysis system, when animage is received for processing. The process starts at block 310.

At block 315, a digital image is received. At block 320, the imagesource is identified. For one embodiment, the image source is identifiedbased on the data header, which specifies the image source. The imagesource may be one of a variety of films, or one of a variety of digitaldetectors. For one embodiment, the image source may further identify themodality of the image, if the image analysis system is a multi-modalanalysis system.

At block 325, the process determines whether the image source providesimages of the “standard canonical form.” For one embodiment, the usersets a standard canonical form, to which all other images are converted.The process determines whether the present image is in that form. Theimage is analyzed to determine whether the responses are that of thestandard canonical form. If the image is in the standard canonical form,the process continues directly to block 340. Otherwise, the processcontinues to block 330. For one embodiment, blocks 320 and 325 may beskipped. In that instance, the remapping is performed regardless of theoriginal format of the image.

At block 330, the appropriate lookup table is retrieved. For oneembodiment, various lookup tables may be stored, for conversion fromvarious formats to the standard canonical form. For another embodiment,if only one image source is used, this step may be skipped, and theprocess may go directly from block 315 to block 335.

At block 335, the image is converted to the standard canonical formatusing the lookup table. For one embodiment, the lookup table is aconversion for each pixel value to a new, adjusted, pixel value. Asdiscussed above, the new adjusted pixel value takes into considerationthe shifting required because of the different response curves, as wellas an offset, if appropriate.

At block 340, the image is passed to the CAD code, for CAD processing.At block 345, the image is processed to identify abnormalities, in thestandard way. The process then ends at block 350.

In this way, a digital image from one of a plurality of sources may bereceived, and remapped into the standard canonical form, for processingby the CAD code. This is advantageous because it does not requireobtaining an extensive database of images from the same source, fortraining, testing, or tuning the CAD code.

FIG. 4 is a flowchart of one embodiment of obtaining a converter forimages from digital response to the canonical. This process is initiatedby the code developer, when a new type of film or digital detector isadded to the system. The process starts at block 410.

At block 415, the canonical image (image A) exposed with a calibrationdesign is received. For one embodiment, this standard canonical formatmay be stored, and thus not actually created at the time of generatingthe lookup table. For another embodiment, the “standard” film is exposedeach time such a lookup table is created. For another embodiment, thestandard canonical format may not be a film-based format. Rather, thestandard canonical format may be a manipulated format. For example, mostfilm-based and digital detectors have an attenuation point, beyond whichthe response is non-linear. The standard canonical form may be a formwhich does not have this attenuation. Thus, receiving the standardcanonical image with the calibration design may include generating theresponse desired.

At block 420, the second image (image B) exposed with a calibrationdesign is received. Image B is the image detected using thedetector/film which is to be remapped to the standard canonical form.

At block 425, pixel values are identified at each “step” in thecalibration design for images A and B. The pixel value (PV) at each stepis designed PVa(1) through PVa(n) for image A, and PVb(1) through PVb(n)for image B.

At block 430, the pixel values of image A are mapped against the pixelvalues of image B. FIG. 8 shows such a mapping. As can be seen PV_(in)is mapped against PV_(canonical).

At block 435, the offset between image A and image B is defined. Theoffset is a constant added to image B to equal image A. The offsetshifts the zero crossing of the response curve.

At block 440, a lookup table is created, mapping each pixel value ofimage B to the “canonical standard form.” This lookup table is thenadded to the transform mapper of the image analysis system. The imageanalysis system then uses the lookup table to remap images received fromthe same detector/film as image B to the standard canonical form. Theprocess then ends at block 445.

The above described apparatus and process permits the analysis of imagesobtained through different imaging mechanisms, using the same CAD code.This eliminates the need for obtaining a large volume of test data fortraining and testing the CAD code for each of the different imagingmechanisms. The imaging mechanism may be a different type of film, or adifferent detector. For example, one imaging mechanism may be using adigital detector. For another embodiment, one imaging mechanism may beusing a new type of film, having a different response than the standardcanonical response.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

1. A computer program product embodied in a computer-readable medium foranalyzing a first medical image to detect anatomical abnormalitiestherein, the first medical image acquired from a first type of imagingsystem having a first response characteristic, comprising:computer-aided detection (CAD) code comprising at least one neuralnetwork-based automated anatomical abnormality detection algorithmtrained using training images acquired from a second type of imagingsystem having a second response characteristic different than the firstresponse characteristic; and transforming code based on said first andsecond response characteristics for transforming pixel values of thefirst medical image to approximate values that would have been obtainedif the first medical image had been acquired using the second type ofimaging system such that said CAD code is applicable for the firstmedical image.
 2. The computer program product of claim 1, wherein saidtransforming code transforms said pixel values using a first lookuptable computed using said first and second response characteristics. 3.The computer program product of claim 2, further comprising a secondlookup table used by said transforming code for transforming pixelvalues of a second medical image acquired from a third type of imagingsystem having a third response characteristic different than said firstand second response characteristics to approximate values that wouldhave been obtained if the second medical image had been acquired usingthe second type of imaging system, whereby said CAD code is alsoapplicable for said second medical image despite having been acquiredusing a different type of imaging system than said training or firstmedical images.
 4. The computer program product of claim 3, wherein saidtransforming code further comprises: identifying code for identifyingwhich type of imaging system was used to acquire each of said medicalimages; a first remapping code for utilizing said first lookup table toremap medical images acquired using said first type of imaging system;and a second remapping code for utilizing said second lookup table toremap medical images acquired using said third type of imaging system.5. The computer program product of claim 4, wherein said identifyingcode further comprises code for reading DICOM headers associated withsaid medical images.
 6. The computer program product of claim 1, whereinsaid first type of imaging system comprises a film-based detector, andwherein said second type of imaging system comprises a digital detector.7. The computer program product of claim 1, wherein said first type ofimaging system comprises a first film-based detector, and wherein saidsecond type of imaging system comprises a second film-based detectordifferent than said first film-based detector.
 8. The computer programproduct of claim 1, wherein said first type of imaging system comprisesa first digital detector, and wherein said second type of imaging systemcomprises a second digital detector different than said first digitaldetector.
 9. The computer program product of claim 1, wherein said firsttype of imaging system comprises a digital detector, and wherein saidsecond type of imaging system comprises a film-based detector.
 10. Amethod for analyzing a first medical image to detect anatomicalabnormalities therein using a computer-aided detection (CAD) algorithmincluding at least one neural network-based automated anatomicalabnormality detection algorithm, the first medical image acquired from afirst type of imaging system having a first response characteristic, theCAD algorithm trained using training images acquired from a second typeof imaging system having a second response characteristic different thanthe first response characteristic, comprising: transforming pixel valuesof the first medical image to approximate pixel values that would havebeen obtained if the first medical image had been acquired using thesecond type of imaging system such that the CAD code is applicable forsaid first medical image.
 11. The method of claim 10, wherein saidtransforming pixel values comprises using a first lookup table designedusing said first and second response characteristics.
 12. The method ofclaim 11, further comprising using a second lookup table fortransforming pixel values of a second medical image acquired from athird type of imaging system having a third response characteristicdifferent than said first and second response characteristics toapproximate values that would have been obtained if the second medicalimage had been acquired using the second type of imaging system suchthat said CAD code is also applicable for said second medical image. 13.The method of claim 11, further comprising: identifying which type ofimaging system was used to acquire each of said medical images; applyingsaid first lookup table for medical images acquired using said firsttype of imaging system; and applying said second lookup table formedical images acquired using said third type of imaging system.
 14. Themethod of claim 13, wherein said identifying further comprises readingDICOM headers associated with said medical images.
 15. The method ofclaim 10, wherein said first type of imaging system comprises afilm-based detector, and wherein said second type of imaging systemcomprises a digital detector.
 16. The method of claim 10, wherein saidfirst type of imaging system comprises a first film-based detector, andwherein said second type of imaging system comprises a second film-baseddetector different than said first film-based detector.
 17. The methodof claim 10, wherein said first type of imaging system comprises a firstdigital detector, and wherein said second type of imaging systemcomprises a second digital detector different than said first digitaldetector.
 18. The method of claim 10, wherein said first type of imagingsystem comprises a digital detector, and wherein said second type ofimaging system comprises a film-based detector.
 19. A method foranalyzing medical images to detect anatomical abnormalities therein,comprising: receiving a medical image; applying a computer-aideddetection (CAD) algorithm to said medical image to detect anatomicalabnormalities therein, wherein the CAD algorithm is a neuralnetwork-based automated anatomical abnormality detection algorithm; andprior to said applying said CAD algorithm, performing the steps of:identifying a first response characteristic associated with anacquisition of the medical image; determining whether the first responsecharacteristic is substantially similar to a second responsecharacteristic associated with an acquisition of a plurality of trainingimages used to train the CAD algorithm; and if said first responsecharacteristic is not substantially similar to said second responsecharacteristic, transforming pixel values of said medical image toapproximate pixel values that would have been obtained if the medicalimage had been acquired in a manner associated with said second responsecharacteristic, such that said CAD code is applicable for the medicalimage.
 20. The method of claim 19, wherein said first responsecharacteristic is similar to a film-based image acquisition responsecharacteristic, and wherein said second response characteristic issimilar to a digital image acquisition response characteristic.
 21. Themethod of claim 19, wherein said first response characteristic issimilar to a first film-based image acquisition response characteristic,and wherein said second response characteristic is similar to a secondfilm-based image acquisition response characteristic.
 22. The method ofclaim 19, wherein said first response characteristic is similar to afirst digital image acquisition response characteristic, and whereinsaid second response characteristic is similar to a second digital imageacquisition response characteristic.
 23. The method of claim 19, whereinsaid first response characteristic is similar to a digital imageacquisition response characteristic, and wherein said second responsecharacteristic is similar to a film-based acquisition responsecharacteristic.